Recent Advances in Predicting Functional Impact of Single Amino Acid Polymorphisms: A Review of Useful Features, Computational Methods and Available Tools

Mingjun      Wang; Zhongwei      Sun; Tatsuya      Akutsu; Jiangning      Song

doi:10.2174/1574893611308020004

Abstract

Owing to the biological significance of single amino acid polymorphism (SAP), there has been an increasing interest in understanding how certain amino acid substitutions give rise to functional change and consequent disease association, while others remain neutral polymorphisms. With the increasing availability of biological data, our knowledge regarding functional elements of the proteome continues to expand. As experimental approaches to characterize specific genetic variants are expensive and time-consuming, it is greatly desirable to develop effective computational methods that are capable of accurately predicting the functional impact of SAPs. In this review, we summarize 22 in silico tools that were previously developed and also discuss the related work of the functional impact prediction of SAPs that did not specifically develop webservers/tools. Procedures regarding how to extract annotations of SAPs and select the relevant useful features, as well as how to choose appropriate algorithms are also described in this review. In the end, a case study is given as an illustration to assess the predictive ability of available tools for predicting the functional consequence of SAPs. It is our hope that this review could serve as a useful guidance for developing nextgeneration in silico approaches for identification of the functional impacts of SAPs in the future.

Keywords: Bioinformatics, computational methods, feature selection, in silico tool, machine learning, sequence analysis, single amino acid polymorphism (SAP), Mutation Prediction, Topographic mapping of Single Nucleotide Polymorphism, Multivariate Analysis of Protein Polymorphism